Multimodal Optimization Using a Bi-Objective Evolutionary Algorithm

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Multimodal Optimization Using a Bi-Objective Evolutionary Algorithm
In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have a better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable optimum solution. To this end, evolutionary optimization algorithms stand as viable methodologies mainly due to their ability to find and capture multiple solutions within a population in a single simulation run. With the preselection method suggested in 1970, there had been a steady suggestion of new algorithms. Most of these methodologies employed a niching scheme in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are de-emphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different strategy in which the single-objective multimodal optimization problem is converted into a...
Kalyanmoy Deb, Amit Saha
Added 21 Apr 2012
Updated 21 Apr 2012
Type Journal
Year 2012
Where EC
Authors Kalyanmoy Deb, Amit Saha
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